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  1. 1
    Modification of the ant colony optimization algorithm for solving multi-agent task allocation problem in agricultural application

    Modification of the ant colony optimization algorithm for solving multi-agent task allocation problem in agricultural application by Hardhienata, Medria Kusuma Dewi, Priandana, Karlisa, Putra, Daffa Rangga, Sriatun, Mamiek, Wulandari, Buono, Agus, Mohamed, Raihani

    Published 2024
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  2. 2
    Global gbest guided-artificial bee colony algorithm for numerical function optimization

    Global gbest guided-artificial bee colony algorithm for numerical function optimization by Shah, Habib, Tairan, Nasser, Garg, Harish, Ghazali, Rozaida

    Published 2018
    “…The two well-known honeybees-based upgraded algorithms, Gbest Guided Artificial Bee Colony (GGABC) and Global Artificial Bee Colony Search (GABCS), use the foraging behavior of the global best and guided best honeybees for solving complex optimization tasks. …”
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  3. 3
    Enhanced grey wolf optimisation algorithm for feature selection in anomaly detection

    Enhanced grey wolf optimisation algorithm for feature selection in anomaly detection by Almazini, Hussein

    Published 2022
    “…The first modification enhances the initial population of the MBGWO using a heuristic based Ant Colony Optimisation algorithm. …”
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  4. 4
    Reactive memory model for ant colony optimization and its application to TSP

    Reactive memory model for ant colony optimization and its application to TSP by Sagban, Rafid, Ku-Mahamud, Ku Ruhana, Abu Bakar, Muhamad Shahbani

    Published 2014
    “…The performances of six (6) ant colony optimization variants were evaluated to select the base for the proposed model.Based on the results, Max-Min Ant System has been chosen as the base for the modification.The modified algorithm called RMMAS, was applied to TSPLIB95 data and results showed that RMMAS outperformed the standard MMAS.…”
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  5. 5
    An approach to improve functional link neural network training using modified artificial bee colony for classification task

    An approach to improve functional link neural network training using modified artificial bee colony for classification task by Yana Mazwin Mohmad Hassim, Rozaida Ghazali

    Published 2013
    “…In this paper, a modified Artificial Bee Colony (mABC) is used to recover the BP drawbacks. …”
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  6. 6
    An approach to improve functional link neural network training using modified artificial bee colony for classification task

    An approach to improve functional link neural network training using modified artificial bee colony for classification task by Mohmad Hassim, Yana Mazwin, Ghazali, Rozaida

    Published 2012
    “…In this paper, a modified Artificial Bee Colony (mABC) is used to recover the BP drawbacks. …”
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  7. 7
    An approach to improve functional link neural network training using modified artificial bee colony for classification task

    An approach to improve functional link neural network training using modified artificial bee colony for classification task by Mohmad Hassim, Yana Mazwin, Ghazali, Rozaida

    Published 2012
    “…In this paper, a modified Artificial Bee Colony (mABC) is used to recover the BP drawbacks. …”
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  8. 8
    A new strategy to improve the search performance of Max-Min Ant Aystem Algorithm when solving the Quadratic Assignment Problems

    A new strategy to improve the search performance of Max-Min Ant Aystem Algorithm when solving the Quadratic Assignment Problems by Phen, Chiak See

    Published 2010
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  9. 9
    RSA Encryption & Decryption using JAVA

    RSA Encryption & Decryption using JAVA by Ramli, Marliyana

    Published 2006
    “…References and theories to support the research of 'RSA Encryption/Decryption using Java' have been disclosed in Literature Review section. …”
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  10. 10
    Balancing exploration and exploitation in ACS algorithms for data clustering

    Balancing exploration and exploitation in ACS algorithms for data clustering by Jabbar, Ayad Mohammed, Sagban, Rafid, Ku-Mahamud, Ku Ruhana

    Published 2019
    “…Ant colony optimization (ACO) is a swarm algorithm inspired by different behaviors of ants. …”
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  11. 11
    Plagiarism Detection System for Java Programming Assignments by Using Greedy String-Tilling Algorithm

    Plagiarism Detection System for Java Programming Assignments by Using Greedy String-Tilling Algorithm by Norulazmi, Kasim

    Published 2008
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  12. 12
    Provider independent cryptographic tools

    Provider independent cryptographic tools by Ibrahim, Subariah, Salleh, Mazleena, Abdul Aziz, Shah Rizan

    Published 2003
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  13. 13
    Enhanced artificial bee colony for training least squares support vector machines in commodity price forecasting

    Enhanced artificial bee colony for training least squares support vector machines in commodity price forecasting by Mustaffa, Zuriani, Yusof, Yuhanis, Kamaruddin, Siti Sakira

    Published 2014
    “…By using commodities prices time series as empirical data, the proposed technique is compared against two techniques, including Back Propagation Neural Network and by Genetic Algorithm.Empirical results show the capability of the proposed technique in producing higher prediction accuracy for the prices of interested time series data.…”
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  14. 14
    A framework of test case prioritisation in regression testing using particle swarm-artificial bee colony algorithm

    A framework of test case prioritisation in regression testing using particle swarm-artificial bee colony algorithm by Ba-Quttayyan, Bakr Salim Abdullah

    Published 2024
    “…TCP factors and a weighted fitness function are also used for test case optimisation. The contributions of this study straddle research perspectives of enhancing Regression Testing with Particle Swarm-Artificial Bee Colony Algorithm, and practical perspectives by providing software testing practitioners the TCP framework that can facilitate and accelerate the production of high-quality software products by revealing faults early and reducing time, cost, and human efforts through automation.…”
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  15. 15
    Bats echolocation-inspired algorithms for global optimisation problems

    Bats echolocation-inspired algorithms for global optimisation problems by Nafrizuan, Mat Yahya

    Published 2016
    “…The algorithm is a hybrid algorithm that operates using dual level search strategy that takes merits of a particle swarm optimisation algorithm and a modified adaptive bats sonar algorithm. …”
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  16. 16
    An improved ACS algorithm for data clustering

    An improved ACS algorithm for data clustering by Mohammed Jabbar, Ayad, Ku-Mahamud, Ku Ruhana, Sagban, Rafid

    Published 2020
    “…Comparison of the performance of several common clustering algorithms using real-world datasets shows that the accuracy results of the proposed algorithm surpasses other algorithms.…”
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  17. 17
    Multi agent quality of service routing based on scheme ant colony optimization algorithm

    Multi agent quality of service routing based on scheme ant colony optimization algorithm by Baygi, Maassoumeh Javadi

    Published 2014
    “…The proposed scheme has been simulated by OMNET++ and compared with standard AntNet and two well-known standard QoS routings; Widest Shortest Path (WSP) algorithm and Shortest Widest Path (SWP) algorithm. …”
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  18. 18
    Real-Time Video Processing Using Native Programming on Android Platform

    Real-Time Video Processing Using Native Programming on Android Platform by Saipullah, Khairul Muzzammil

    Published 2012
    “…However for the Android platform that based on the JAVA language, most of the software algorithm is running on JAVA that consumes more time to be compiled. …”
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  19. 19
    Direct approach for mining association rules from structured XML data

    Direct approach for mining association rules from structured XML data by Abazeed, Ashraf Riad

    Published 2012
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  20. 20
    Predictive analysis of dengue outbreak based on an improved salp swarm algorithm

    Predictive analysis of dengue outbreak based on an improved salp swarm algorithm by Zuriani, Mustaffa, M. H., Sulaiman, Khairunnisa Amalina, Mohd Rosli, Mohamad Farhan, Mohamad Mohsin, Yuhanis, Yusof

    Published 2020
    “…The efficiency of the proposed algorithm is evaluated using Root Mean Square Error (RMSE), and compared against the conventional SSA and Ant Colony Optimization (ACO). …”
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